Deal probability brings a dose of reality to your sales forecasts. Turning on probability in Pipedrive not only gives you a more sophisticated data set but its standardized and automated nature means that the data you see is more likely to be up-to-date and usable for management decisions.

## An Overview of Deal Probability

Deal probability, also known as "deal weighting," is the practice of assigning a percentage to the likelihood that a deal will successfully close based on stage of the sales pipeline where it currently sits.

For example, let's say you have a simple, six-stage sales pipeline.

2) Qualified via discovery call

3) Product demo

4) Proposal provided

5) In negotiation

6) Deal is won

Let's say your typical deal is \$100,000; you could assign a potential deal value of \$100,000 to the leads in every stage of your pipeline. The issue, of course, is that you don't want to assign every lead a 'worth' of \$100,000 when, in the end, just a few of those leads will make it all the way through to become a completed deal.

In order to create more accurate sales forecasts it is useful to assign a probability weighting to each stage of the pipeline in order to bring some sense of reality to the figures.

Let's take another look at our sales pipeline above.

If we had 20 deals in various stages of the pipeline, each valued at \$100,000, there is potential revenue of \$2,000,000.

1) Lead is identified: 7 deals valued at \$700,000

2) Qualified via discovery call: 4 deals at \$400,000

3) Product demo: 3 deals at \$300,000

4) Proposal provided 2 deals at \$200,000

5) In negotiation 1 deal at \$100,000

6) Deal is won 3 deals at \$300,000

Total: \$2,000,000

A closer look at this pipeline will reveal that over one third of the deals (those in stage one) are leads that haven't even been contacted yet to be qualified as a bonafide prospect. Let's say that only one out of ten of these leads are even qualified on average, it is completely misleading to have them each valued at \$100,000.

In fact, until a prospect has been contacted and qualified, there's likely too little information to even assign a value or predict the chance of closing those deals.

The next stage currently contains deals worth an assigned value of \$400,000. Although the initial call went well with the lead and they are interested in learning more about your product, the likelihood of closing a deal after this initial call is still low, but assigning a likelihood of closing a deal at this point is reasonable.

How accurate will the prediction be? That really depends on a few factors, such as the number of deals your business has closed in the past and how well you've tracked that data to be used in your forecast. The more you've closed and the more accurately the deal stages have been tracked, the more accurate your probability weighting will be.

### Calculating Probability

Calculating probability is as simple as assigning a percentage to the deals in a stage. For instance, let's say that you tend to close around 10% of the qualified prospects in stage two of your pipeline and you have \$400,000 of potential deals in that stage, then your weighted deals would be worth \$40,000 not \$400,000.

Similarly, we can assign percentages to each stage of the pipeline and we will see a much different picture emerge:

1) Lead is identified - 7 deals valued at \$700,000 x 0% = \$0

2) Qualified via discovery call - 4 deals at \$400,000 x 10% probability = \$40,000

3) Product demo - 3 deals at \$300,000 x 30% probability = \$100,000

4) Proposal provided - 2 deals at \$200,000 x 50% probability of closing = \$100,000

5) In negotiation - 1 deal at \$100,000 x 90% probability of closing = \$90,000

6) Deal is won - 3 deals at 100% = \$300,000

Total: \$630,000 of \$2,000,000 in potential deals

Now the numbers reflect a weighted revenue and are more realistic. Instead of predicting \$2,000,000 in sales, the weighted value shows expected revenue of \$630,000 - less than a third of total deals.

### The Benefits of Using Deal Probability

The process of assigning deal probability to 'weight' a stage in the sales process can provide a much clearer picture of the actual sales/revenue likely to be gained from a set of deals.

Since it is critical for businesses to have an accurate and realistic forecast of their revenue, deal weighting can help both sales managers and management more accurately assess their progress versus their sales goals.

On a micro level, the data can provide a set of metrics that may help evaluate individual sales staff. It can be useful to see how different sales staff close at specific stages of the sales pipeline compared to historic closing rates. This data can help you assess training needs and opportunities to refine your overall pipeline.

For management/ownership, weighted sales forecasts can provide a crisper assessment of where sales are expected to go versus the businesses revenue goals. That can help management diagnose problems such as potential capacity or cash flow issues ahead of time, giving them time to proactively react .

### Weaknesses in Deal Probability

There has been some criticism of using deal probability in your sales forecast due to its inherent inaccuracy. It is, a forecast after all. But, simply put, using deal probability is significantly more accurate than tracking the total value of deals and assigning weight to stages is both efficient and relatively accurate versus the alternatives.

...deal probability can not only set a reasonably accurate projection for closing deals, but its highly efficient implementation provides easily accessible data

One suggested alternative to assigning general probability to stages is to evaluate individual deals. In this approach, sales staff are tasked with making judgement calls on the likelihood that an individual deal will close.

Although this approach may provide more accuracy (my personal opinion is that is unlikely) it has several key issues that plague that approach.

First, even if the approach was proven more accurate, the lack of a reliable and automated reporting process, as is seen in the probability systems in Pipedrive, means that the data is not nearly as accessible. In other words, if (or should I say "when") sales staff fail to update deal probability manually as soon as a deal appears to progress forward, the accuracy of the data is compromised.

In addition, there really isn't a reasonable way to manually update the probability per deal, reflect that in the value of the deal and report that up to management in a seamless, efficient and up-to-the-minute manner.

The second, more glaring issue is that evaluating individual deals brings in a big dose of confirmation bias combined with a lack of standardization in the estimation of closing rates.

Asking a salesperson, 'what are the chances this deal closes' might feel right, but the amount of experience a person has combined with their overall mood that day can lead to widely ineffective guesses versus data gleaned from a large number of past deals and documented conversion rates.

My suggestion is that deal probability can not only set a reasonably accurate projection for closing deals, but its highly efficient implementation provides easily accessible data that helps in the business decision process from the sales team all the way through to the C-suite.

However, in order to maintain accuracy, a constant evaluation of the closing rates, updating the stage probabilities and evaluating input from your sales team can give the best set of data with the least friction possible.

### Making it Effective

So, in practical terms, how can we make deal probability more effective?

The first factor is the accuracy applied to the stages. If you haven't been tracking the closing rates or have not locked down a set of stages that meet your needs quite yet, then you will have to start with your best educated guess. Whether or not you have a significant number of deals to track, make sure that you check on the rates regularly and adjust the figures as the picture becomes more refined. The higher the volume of deals in your calculations, the more accurate your rates will be.

Recognize the numbers for what they are: a forecast. It'll never be 100% accurate so err on the side of caution when you are setting goals and judging sales performance. The larger the set of data and longer the timeframe, the more accurate the correlations will likely be.

Finally, a key factor that will lead to more accurate forecasts is a rigorously defined set of criteria for what constitutes a stage, and the requirements to move a prospect forward in your sales process. In the end, it is still up to individual sales staff to judge what stage a prospect is at in the sales cycle.

Some stages are inherently more clear but in some cases there's much more room to differ from salesperson to salesperson. For instance, what constitutes moving a lead to a qualified prospect based on a phone call? What questions need to be answered? What criteria was met to move a lead forward? [https://blog.pipedrive.com/2015/01/how-to-do-lead-qualification-right-this-software-company-gets-it/]

### Pipedrive Deal Probability

So, if you've hung in this long, I'm guessing you're interested in giving this deal probability thing a try in Pipedrive.

Here's how to get started:

1. Turn on Deal Probability in your Pipedrive Settings, click on 'Features'

2. Apply probability to stages in your pipelines as percentages

...you adjust each stage in each pipeline separately

3. View the probability numbers in your pipelines

### Using Dryrun with Pipedrive to Build Sophisticated Forecasts in a Click

Dryrun is a powerful and intuitive financial forecasting tool that integrates seamlessly with Pipedrive. Dryrun helps sales staff, sales managers and business managers all maintain an up-to-date revenue forecast.

Track individual pipelines, deals and closing dates to analyze granular data, or view the big picture of your entire pipeline. View your deals with probability on, off, or view only 'Won' deals.

The unique scenario-based approach, lets you evaluate different potential outcomes, test your assumptions and identify issues ahead of time - all in just a few clicks.

Powerful modelling comes with the manual control and flexibility allow you to move things around, add/remove/edit data without affecting your Pipedrive data.

From a business management level, you can even work in an ongoing, automated budget forecast and track your Actuals that affect your cash flow, giving you a true 360º view of your future finances.

### Benefits of a Pipedrive Sales Forecast Viewed in Dryrun

For sales managers, a Pipedrive integration with Dryrun offers a way to view both the big picture of your sales forecast and to focus on individual pipelines and sales staff. With deal probability views turned on, it's easy to see gaps in the sales pipeline well ahead of time, identify weaknesses and opportunities at a glance and model out different potential outcomes to inform decisions.

The straight-forward and elegant collaboration features in Dryrun offer a seamless way for both sales staff and management to stay informed and up-to-the-minute.

Business management can identify potential revenue shortfalls and cash flow crunches ahead of time. They may even identify potential capacity issues with plenty of time to react. Clear and accessible data also offers the potential to discover areas of opportunity and untapped channels.

Dryrun's power and flexibility lets users extend the use well outside of sales forecast to include a detailed view of the sales forecasts alongside an ongoing budget and cash flow forecast for a true 360 view of your financial forecasts.

Pipedrive deal probability is a powerful and essential tool for sales managers, business owners and management teams to make near term tactical and long term strategic business decisions.